title: A [-2]proPSA-based artificial neural network significantly improves differentiation between prostate cancer and benign prostatic diseases.
abstract: BACKGROUND
  The aim of this study was to combine the new automated Access [-2]proPSA (p2PSA) assay with a percent free PSA (%fPSA) based artificial neural network (ANN) or logistic regression (LR) model to enhance discrimination between patients with prostate cancer (PCa) and with no evidence of malignancy (NEM) and to detect aggressive PCa.
  
  
  METHODS
  Sera from 311 PCa patients and 275 NEM patients were measured with the p2PSA, total PSA (tPSA) and free PSA (fPSA) assays on Access immunoassay technology (Beckman Coulter, Fullerton, CA) within the 0-30 ng/ml tPSA range. Four hundred seventy-five patients (264 PCa, 211 NEM) had a tPSA of 2-10 ng/ml. LR models and leave-one-out (LOO) ANN models with Bayesian regularization by using tPSA, %fPSA, p2PSA/fPSA (%p2PSA), age and prostate volume were constructed and compared by receiver-operating characteristic (ROC) curve analysis.
  
  
  RESULTS
  The ANN and LR model each utilizing %p2PSA, %fPSA, tPSA and age, but without prostate volume, reached the highest AUCs (0.85 and 0.84) and best specificities (ANN: 62.1% and 45.5%; LR: 53.1% and 41.2%) compared with tPSA (22.7% and 11.4%) and %fPSA (45.5% and 26.1%) at 90% and 95% sensitivity. The %p2PSA furthermore distinguished better than tPSA and %fPSA between pT2 and pT3, and Gleason sum <7 and >or=7 PCa.
  
  
  CONCLUSIONS
  The automated p2PSA assay offers a new tool to improve PCa detection, and especially aggressive PCa detection. Incorporation of %p2PSA into an ANN and LR model further enhances the diagnostic accuracy to differentiate between malignant and non-malignant prostate diseases.
authors.affiliation: Department of Urology, Charité-Universitätsmedizin Berlin, Berlin, Germany. carsten.stephan@charite.de
authors.forename: Carsten
authors.initials: C
authors.lastname: Stephan
authors.forename: Anna-Maria
authors.initials: AM
authors.lastname: Kahrs
authors.forename: Henning
authors.initials: H
authors.lastname: Cammann
authors.forename: Michael
authors.initials: M
authors.lastname: Lein
authors.forename: Mark
authors.initials: M
authors.lastname: Schrader
authors.forename: Serdar
authors.initials: S
authors.lastname: Deger
authors.forename: Kurt
authors.initials: K
authors.lastname: Miller
authors.forename: Klaus
authors.initials: K
authors.lastname: Jung
mesh_terms: D000368:Aged; D001331:Automation / Q000379:methods; D001604:Berlin; D006801:Humans; D007118:Immunoassay / Q000379:methods; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011467:Prostate / Q000033:anatomy & histology / Q000473:pathology; D017430:Prostate-Specific Antigen / Q000032:analysis*; D011469:Prostatic Diseases / Q000175:diagnosis / Q000473:pathology*; D011470:Prostatic Hyperplasia / Q000473:pathology; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000473:pathology*; D012044:Regression Analysis; D018401:Sample Size; D012680:Sensitivity and Specificity
chemical_list: D017430:Prostate-Specific Antigen